System

Abstract

An object of the system according to the embodiment is to efficiently support the work execution of a new employee or a mid-process employee and to appropriately measure the growth thereof.SOLUTION: A system according to an embodiment includes a work assistance unit and a growth measurement unit. The work assistance unit provides information and procedures necessary for a new employee or a mid-process employee to perform work. A growth measuring part monitors the job execution conditions of the new employee and the halfway employee and measures the growth.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently support the work of new and mid-career employees and to properly measure their growth.

[0005] The system according to the embodiment aims to efficiently support the work performance of new employees and mid-career employees and to appropriately measure their growth. [Means for solving the problem]

[0006] The system according to the embodiment includes a work support unit and a growth measurement unit. The work support unit provides new employees and mid-career employees with the information and procedures they need to perform their work. The growth measurement unit monitors the work performance of new employees and mid-career employees and measures their growth. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently support the work performance of new employees and mid-career employees and appropriately measure their growth. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Elder's auxiliary AI service according to an embodiment of the present invention is a system that supports the work of new employees and mid-career employees and measures their growth. As a result, the Elder's auxiliary AI service can efficiently provide work assistance and growth measurement for new employees and mid-career employees.

[0029] An elder assistance AI service according to an embodiment includes a generation AI, a task assistance unit, and a growth measurement unit. The generation AI provides new employees and mid-career employees with the information and procedures they need to perform their tasks. For example, when a new employee performs a task for the first time, the generation AI presents specific steps, such as, "To perform this task, first do A, then do B." The generation AI also provides appropriate information based on prompts containing instructions from the user regarding what the user wants the generation AI to do. The growth measurement unit monitors the task performance of new employees and mid-career employees and measures their growth. For example, the generation AI analyzes task progress and deliverables and provides growth indicators, such as, "This employee's work efficiency has improved by 20% over the past month." The generation AI measures growth based on data related to task progress and deliverables. This allows the elder assistance AI service according to an embodiment to efficiently provide task assistance and growth measurement for new employees and mid-career employees.

[0030] The business support unit can refer to the user's past work history and provide individually customized procedures. In the business support unit, for example, the generation AI analyzes the user's past work history and provides individually customized procedures. For example, it presents optimal procedures based on examples of success and failure in past work. In addition, the business support unit can provide individually customized procedures based on the user's work history. For example, it can refer to performance data from past work and suggest efficient procedures. In addition, the business support unit can provide individually customized procedures based on the user's past work history. For example, it can present procedures based on the user's experience and skill level in a specific work. This makes it possible to provide optimal procedures based on the user's past work history.

[0031] The task support unit can collect real-time feedback from the user and dynamically adjust the procedures. In the task support unit, for example, the generation AI collects real-time feedback from the user and dynamically adjusts the procedures. For example, if the user finds a procedure difficult, the procedure is immediately changed. In addition, in the task support unit, the generation AI dynamically adjusts the procedures based on the user's real-time feedback. For example, the process analyzes the user's progress and reactions and provides the optimal procedure. In addition, in the task support unit, the generation AI collects real-time feedback from the user and dynamically adjusts the procedures. For example, the process changes the difficulty or order of the procedures based on the user's feedback. This makes it possible to dynamically adjust the procedures based on the user's real-time feedback.

[0032] The business support unit can add a voice assistant function to respond to voice instructions and questions. The business support unit, for example, adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user asks a question by voice, the generation AI provides a voice answer. The business support unit also uses the voice assistant function to have the generation AI respond to voice instructions and questions when assisting with business. For example, when a user gives instructions by voice, the generation AI explains the procedure by voice. The business support unit also adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user reports the progress of work by voice, the generation AI provides feedback by voice. This makes it possible to respond to voice instructions and questions.

[0033] The business support department can integrate a chat function to promote collaboration with other employees. For example, the business support department integrates a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI poses questions to other employees through chat. The business support department also uses the chat function to promote collaboration with other employees when the generation AI is assisting with work. For example, the generation AI shares the progress of work with other employees through chat. The business support department also integrates a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI collects feedback from other employees through chat. This can promote collaboration with other employees.

[0034] The growth measurement unit can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. In the growth measurement unit, for example, the generation AI integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth is measured by combining the self-assessment score with evaluations by superiors and colleagues. The growth measurement unit also analyzes the user's self-assessment and evaluations by others, and the generation AI provides a comprehensive growth indicator. For example, the content of the self-assessment is integrated with feedback from evaluations by others. The growth measurement unit also integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth is measured by combining strengths in the self-assessment and areas for improvement in evaluations by others. In this way, the user's self-assessment and evaluations by others can be integrated to provide a comprehensive growth indicator.

[0035] The growth measurement unit can measure growth by taking into account the user's activities outside of work. In the growth measurement unit, for example, the generation AI measures growth by taking into account the user's activities outside of work. For example, it provides a growth index based on the participation history of training and study sessions. The growth measurement unit also collects data on activities outside of work, which the generation AI reflects in the growth measurement. For example, it takes into account the content of seminars and workshops that the user has attended. In addition, the growth measurement unit measures growth by taking into account the user's activities outside of work. For example, it includes self-study and hobby activities in the growth index. This makes it possible to measure growth by taking into account the user's activities outside of work.

[0036] The growth measurement unit can provide comparative data with other employees and evaluate relative growth. In the growth measurement unit, for example, the generation AI provides comparative data with other employees and evaluates relative growth. For example, it compares the growth data of other employees who are in charge of the same work. In addition, the growth measurement unit has the generation AI evaluate the user's relative growth based on the growth data of other employees. For example, it compares growth rates over the same period. In addition, the growth measurement unit has the generation AI provide comparative data with other employees and evaluate relative growth. For example, it analyzes the growth difference with employees who have the same skill set. This allows the generation AI to provide comparative data with other employees and evaluate relative growth.

[0037] The growth measurement unit can suggest a career path according to the user's growth. In the growth measurement unit, for example, the generation AI suggests a career path according to the user's growth based on the user's growth data. For example, it presents the next skills to be acquired and the position to aim for. The growth measurement unit also analyzes the user's growth data and the generation AI suggests a career path. For example, it suggests an appropriate career step according to the degree of growth. The growth measurement unit also suggests a career path according to the user's growth based on the generation AI. For example, it sets future career goals based on growth indicators. This makes it possible to suggest a career path according to the user's growth.

[0038] The growth measurement unit can take the user's work environment into consideration when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's work environment and evaluates the work performance status. For example, it measures efficiency based on the organization of the desk and work time. The growth measurement unit also collects work environment data and the generation AI monitors the work performance status. For example, it takes into consideration the degree of organization of the user's work space and the length of work time. The growth measurement unit also monitors the user's work environment and evaluates the work performance status. For example, it measures productivity based on the organization of the desk and work time. This makes it possible to monitor work performance status taking the user's work environment into consideration.

[0039] The growth measurement unit can take the user's health condition into consideration when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's health condition and evaluates work performance status. For example, it measures productivity based on sleep time and exercise amount. The growth measurement unit also collects health condition data and the generation AI monitors work performance status. For example, it evaluates work efficiency taking into consideration the user's sleep time and exercise amount. The growth measurement unit also monitors the user's health condition and evaluates work performance status. For example, it measures work performance based on sleep time and exercise amount. This makes it possible to monitor work performance taking the user's health condition into consideration.

[0040] The growth measurement unit can take into account the communication status with other employees when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's communication status and evaluates the work performance status. For example, the evaluation is based on the frequency of chats and emails with other employees. The growth measurement unit also collects communication data and the generation AI monitors the work performance status. For example, the number of meetings and consultations with other employees is taken into account. In the growth measurement unit, the generation AI monitors the user's communication status and evaluates the work performance status. For example, the evaluation is based on the degree of cooperation with other employees and the frequency of feedback. This makes it possible to monitor work performance status taking into account the communication status with other employees.

[0041] The growth measurement unit can take into account the user's activities outside of work when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's activities outside of work and evaluates the work performance status. For example, the evaluation is based on the time spent on volunteer activities or hobbies. The growth measurement unit also collects data on activities outside of work and the generation AI monitors the work performance status. For example, the content of the user's volunteer activities or hobbies is taken into consideration. In addition, the growth measurement unit also monitors the user's activities outside of work and evaluates the work performance status. For example, productivity is measured based on the time spent on volunteer activities or hobbies. This makes it possible to monitor work performance status taking into account the user's activities outside of work.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The business support unit can refer to the user's past work history and provide individually customized procedures. For example, it can present optimal procedures based on examples of success and failure in past work. The business support unit also allows the generation AI to provide individually customized procedures based on the user's work history. For example, it can refer to performance data from past work and suggest efficient procedures. The business support unit also allows the generation AI to refer to the user's past work history and provide individually customized procedures. For example, it can present procedures based on the user's experience and skill level in a specific work. This makes it possible to provide optimal procedures based on the user's past work history.

[0044] The task support unit can collect real-time feedback from the user and dynamically adjust the procedures. For example, if the user finds a procedure difficult, the procedure can be changed immediately. In addition, the task support unit allows the generation AI to dynamically adjust the procedures based on the user's real-time feedback. For example, it analyzes the user's progress and reactions and provides the optimal procedure. In addition, the task support unit allows the generation AI to collect real-time feedback from the user and dynamically adjust the procedures. For example, it changes the difficulty or order of the procedures based on the user's feedback. This makes it possible to dynamically adjust the procedures based on the user's real-time feedback.

[0045] The business support department can add a voice assistant function to respond to voice instructions and questions. For example, when a user asks a question by voice, the generation AI provides a voice answer. The business support department also uses the voice assistant function to have the generation AI respond to voice instructions and questions when assisting with business. For example, when a user gives instructions by voice, the generation AI explains the procedure by voice. The business support department also adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user reports the progress of work by voice, the generation AI provides feedback by voice. This makes it possible to respond to voice instructions and questions.

[0046] The business support department can integrate a chat function to promote collaboration with other employees. For example, a chat function can be integrated into the generation AI to promote collaboration with other employees. For example, the generation AI can ask questions to other employees through chat. The business support department can also use the chat function to promote collaboration with other employees when the generation AI is assisting with work. For example, the generation AI can share the progress of work with other employees through chat. The business support department can also integrate a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI can collect feedback from other employees through chat. This can promote collaboration with other employees.

[0047] The growth measurement unit can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, the generation AI can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth can be measured by combining the self-assessment score with evaluations by superiors and colleagues. The growth measurement unit also analyzes the user's self-assessment and evaluations by others, and the generation AI can provide a comprehensive growth indicator. For example, the content of the self-assessment can be integrated with feedback from evaluations by others. The growth measurement unit also integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth can be measured by combining strengths in the self-assessment and areas for improvement in evaluations by others. In this way, the user's self-assessment and evaluations by others can be integrated to provide a comprehensive growth indicator.

[0048] The growth measurement unit can measure growth by taking into account the user's activities outside of work. For example, the generation AI can measure growth by taking into account the user's activities outside of work. For example, it can provide growth indicators based on participation history in training and study sessions. The growth measurement unit also collects data on activities outside of work, which the generation AI can reflect in growth measurement. For example, it can take into account the content of seminars and workshops that the user has attended. The growth measurement unit can also measure growth by taking into account the user's activities outside of work. For example, it can include self-study and hobby activities in the growth indicators. This allows growth measurement to be performed by taking into account the user's activities outside of work.

[0049] The growth measurement unit can provide comparative data with other employees and evaluate relative growth. For example, the generation AI can provide comparative data with other employees and evaluate relative growth. For example, it can compare with the growth data of other employees who are in charge of the same work. The growth measurement unit also allows the generation AI to evaluate the user's relative growth based on the growth data of other employees. For example, it can compare growth rates over the same period. The growth measurement unit also allows the generation AI to provide comparative data with other employees and evaluate relative growth. For example, it can analyze the difference in growth with employees who have the same skill set. This allows it to provide comparative data with other employees and evaluate relative growth.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The work support department provides new employees and mid-career employees with the information and procedures they need to carry out their work. For example, when a new employee performs a task for the first time, the generation AI presents specific steps such as, "To perform this task, first do A, then do B." The generation AI also provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The Growth Measurement Department monitors the work performance of new and mid-career employees and measures their growth. For example, the Generative AI analyzes work progress and deliverables and provides growth indicators such as, "This employee's work efficiency has improved by 20% over the past month." The Generative AI measures growth based on data on work progress and deliverables.

[0052] (Example 2) The Elder's auxiliary AI service according to an embodiment of the present invention is a system that supports the work of new employees and mid-career employees and measures their growth. As a result, the Elder's auxiliary AI service can efficiently provide work assistance and growth measurement for new employees and mid-career employees.

[0053] An elder assistance AI service according to an embodiment includes a generation AI, a task assistance unit, and a growth measurement unit. The generation AI provides new employees and mid-career employees with the information and procedures they need to perform their tasks. For example, when a new employee performs a task for the first time, the generation AI presents specific steps, such as, "To perform this task, first do A, then do B." The generation AI also provides appropriate information based on prompts containing instructions from the user regarding what the user wants the generation AI to do. The growth measurement unit monitors the task performance of new employees and mid-career employees and measures their growth. For example, the generation AI analyzes task progress and deliverables and provides growth indicators, such as, "This employee's work efficiency has improved by 20% over the past month." The generation AI measures growth based on data related to task progress and deliverables. This allows the elder assistance AI service according to an embodiment to efficiently provide task assistance and growth measurement for new employees and mid-career employees.

[0054] The business support unit can refer to the user's past work history and provide individually customized procedures. In the business support unit, for example, the generation AI analyzes the user's past work history and provides individually customized procedures. For example, it presents optimal procedures based on examples of success and failure in past work. In addition, the business support unit can provide individually customized procedures based on the user's work history. For example, it can refer to performance data from past work and suggest efficient procedures. In addition, the business support unit can provide individually customized procedures based on the user's past work history. For example, it can present procedures based on the user's experience and skill level in a specific work. This makes it possible to provide optimal procedures based on the user's past work history.

[0055] The task support unit can collect real-time feedback from the user and dynamically adjust the procedures. In the task support unit, for example, the generation AI collects real-time feedback from the user and dynamically adjusts the procedures. For example, if the user finds a procedure difficult, the procedure is immediately changed. In addition, in the task support unit, the generation AI dynamically adjusts the procedures based on the user's real-time feedback. For example, the process analyzes the user's progress and reactions and provides the optimal procedure. In addition, in the task support unit, the generation AI collects real-time feedback from the user and dynamically adjusts the procedures. For example, the process changes the difficulty or order of the procedures based on the user's feedback. This makes it possible to dynamically adjust the procedures based on the user's real-time feedback.

[0056] The business support unit can estimate the user's stress level using the emotion estimation function and suggest relaxation methods if stress is high. For example, the business support unit can estimate the user's stress level using the emotion estimation function and suggest relaxation methods if stress is high. For example, it can recommend deep breathing or a short break. The business support unit also monitors the user's stress level in real time using the generation AI and suggests relaxation methods if stress is high. For example, it can play relaxing music or provide a meditation guide. The business support unit also estimates the user's stress level using the emotion estimation function and suggest relaxation methods if stress is high. For example, it can recommend stretching or light exercise. This makes it possible to suggest relaxation methods according to the user's stress level.

[0057] The business support unit can add a voice assistant function to respond to voice instructions and questions. The business support unit, for example, adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user asks a question by voice, the generation AI provides a voice answer. The business support unit also uses the voice assistant function to have the generation AI respond to voice instructions and questions when assisting with business. For example, when a user gives instructions by voice, the generation AI explains the procedure by voice. The business support unit also adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user reports the progress of work by voice, the generation AI provides feedback by voice. This makes it possible to respond to voice instructions and questions.

[0058] The business support department can integrate a chat function to promote collaboration with other employees. For example, the business support department integrates a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI poses questions to other employees through chat. The business support department also uses the chat function to promote collaboration with other employees when the generation AI is assisting with work. For example, the generation AI shares the progress of work with other employees through chat. The business support department also integrates a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI collects feedback from other employees through chat. This can promote collaboration with other employees.

[0059] The work support unit can use the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, the work support unit uses the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, it sends an encouraging message such as "Great job!". The work support unit also monitors the user's emotional state using the generation AI and provides messages that increase motivation so that the user feels positive about their work. For example, it sends a message such as "Your efforts are paying off." The work support unit also uses the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, it sends a message such as "I can see your growth." In this way, it is possible to provide messages that increase the user's motivation.

[0060] The growth measurement unit can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. In the growth measurement unit, for example, the generation AI integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth is measured by combining the self-assessment score with evaluations by superiors and colleagues. The growth measurement unit also analyzes the user's self-assessment and evaluations by others, and the generation AI provides a comprehensive growth indicator. For example, the content of the self-assessment is integrated with feedback from evaluations by others. The growth measurement unit also integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth is measured by combining strengths in the self-assessment and areas for improvement in evaluations by others. In this way, the user's self-assessment and evaluations by others can be integrated to provide a comprehensive growth indicator.

[0061] The growth measurement unit can measure growth by taking into account the user's activities outside of work. In the growth measurement unit, for example, the generation AI measures growth by taking into account the user's activities outside of work. For example, it provides a growth index based on the participation history of training and study sessions. The growth measurement unit also collects data on activities outside of work, which the generation AI reflects in the growth measurement. For example, it takes into account the content of seminars and workshops that the user has attended. In addition, the growth measurement unit measures growth by taking into account the user's activities outside of work. For example, it includes self-study and hobby activities in the growth index. This makes it possible to measure growth by taking into account the user's activities outside of work.

[0062] The growth measurement unit can measure the user's motivation and satisfaction using the emotion estimation function and reflect it in the growth index. The growth measurement unit can, for example, use the emotion estimation function to measure the user's motivation and satisfaction and reflect it in the growth index. For example, it evaluates the degree of growth based on the user's emotion score. The growth measurement unit also monitors the user's motivation and satisfaction in real time using the generation AI and reflects it in the growth index. For example, it determines that growth is progressing when there are many positive emotions. The growth measurement unit can also measure the user's motivation and satisfaction using the emotion estimation function and reflect it in the growth index. For example, it evaluates the progress of growth based on changes in the user's emotions. This makes it possible to reflect the user's motivation and satisfaction in the growth index.

[0063] The growth measurement unit can provide comparative data with other employees and evaluate relative growth. In the growth measurement unit, for example, the generation AI provides comparative data with other employees and evaluates relative growth. For example, it compares the growth data of other employees who are in charge of the same work. In addition, the growth measurement unit has the generation AI evaluate the user's relative growth based on the growth data of other employees. For example, it compares growth rates over the same period. In addition, the growth measurement unit has the generation AI provide comparative data with other employees and evaluate relative growth. For example, it analyzes the growth difference with employees who have the same skill set. This allows the generation AI to provide comparative data with other employees and evaluate relative growth.

[0064] The growth measurement unit can suggest a career path according to the user's growth. In the growth measurement unit, for example, the generation AI suggests a career path according to the user's growth based on the user's growth data. For example, it presents the next skills to be acquired and the position to aim for. The growth measurement unit also analyzes the user's growth data and the generation AI suggests a career path. For example, it suggests an appropriate career step according to the degree of growth. The growth measurement unit also suggests a career path according to the user's growth based on the generation AI. For example, it sets future career goals based on growth indicators. This makes it possible to suggest a career path according to the user's growth.

[0065] The growth measurement unit can provide positive feedback using the emotion estimation function so that the user can realize their growth. The growth measurement unit, for example, uses the emotion estimation function to provide positive feedback so that the user can realize their growth. For example, it sends a message such as, "I can see your growth." The growth measurement unit also monitors the user's emotional state and provides positive feedback so that the user can realize their growth. For example, it sends a message such as, "Great progress." The growth measurement unit also uses the emotion estimation function to provide positive feedback so that the user can realize their growth. For example, it sends a message such as, "Your efforts are paying off." In this way, it is possible to provide positive feedback so that the user can realize their growth.

[0066] The growth measurement unit can take the user's work environment into consideration when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's work environment and evaluates the work performance status. For example, it measures efficiency based on the organization of the desk and work time. The growth measurement unit also collects work environment data and the generation AI monitors the work performance status. For example, it takes into consideration the degree of organization of the user's work space and the length of work time. The growth measurement unit also monitors the user's work environment and evaluates the work performance status. For example, it measures productivity based on the organization of the desk and work time. This makes it possible to monitor work performance status taking the user's work environment into consideration.

[0067] The growth measurement unit can take the user's health condition into consideration when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's health condition and evaluates work performance status. For example, it measures productivity based on sleep time and exercise amount. The growth measurement unit also collects health condition data and the generation AI monitors work performance status. For example, it evaluates work efficiency taking into consideration the user's sleep time and exercise amount. The growth measurement unit also monitors the user's health condition and evaluates work performance status. For example, it measures work performance based on sleep time and exercise amount. This makes it possible to monitor work performance taking the user's health condition into consideration.

[0068] The growth measurement unit can monitor the user's emotional state using the emotion estimation function and provide support according to changes in emotions. For example, the growth measurement unit can monitor the user's emotional state using the emotion estimation function and provide support according to changes in emotions. For example, if stress is high, it can suggest relaxation methods. The growth measurement unit also monitors the user's emotional state in real time using the generation AI and provides support according to changes in emotions. For example, it can provide advice on how to maintain positive emotions. The growth measurement unit also monitors the user's emotional state using the emotion estimation function and provides support according to changes in emotions. For example, if negative emotions are strong, it can send an encouraging message. This makes it possible to provide support according to the user's emotional state.

[0069] The growth measurement unit can take into account the communication status with other employees when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's communication status and evaluates the work performance status. For example, the evaluation is based on the frequency of chats and emails with other employees. The growth measurement unit also collects communication data and the generation AI monitors the work performance status. For example, the number of meetings and consultations with other employees is taken into account. In the growth measurement unit, the generation AI monitors the user's communication status and evaluates the work performance status. For example, the evaluation is based on the degree of cooperation with other employees and the frequency of feedback. This makes it possible to monitor work performance status taking into account the communication status with other employees.

[0070] The growth measurement unit can take into account the user's activities outside of work when monitoring work performance status. In the growth measurement unit, for example, the generation AI monitors the user's activities outside of work and evaluates the work performance status. For example, the evaluation is based on the time spent on volunteer activities or hobbies. The growth measurement unit also collects data on activities outside of work and the generation AI monitors the work performance status. For example, the content of the user's volunteer activities or hobbies is taken into consideration. In addition, the growth measurement unit also monitors the user's activities outside of work and evaluates the work performance status. For example, productivity is measured based on the time spent on volunteer activities or hobbies. This makes it possible to monitor work performance status taking into account the user's activities outside of work.

[0071] The growth measurement unit can use the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, the growth measurement unit uses the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, it sends an encouraging message such as "Great job!". The growth measurement unit also monitors the user's emotional state using the generation AI and provides messages that increase motivation so that the user feels positive about their work. For example, it sends a message such as "Your efforts are paying off." The growth measurement unit also uses the emotion estimation function to provide messages that increase motivation so that the user feels positive about their work. For example, it sends a message such as "I can see your growth." In this way, it is possible to provide messages that increase motivation so that the user feels positive about their work.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The business support unit can refer to the user's past work history and provide individually customized procedures. For example, it can present optimal procedures based on examples of success and failure in past work. The business support unit also allows the generation AI to provide individually customized procedures based on the user's work history. For example, it can refer to performance data from past work and suggest efficient procedures. The business support unit also allows the generation AI to refer to the user's past work history and provide individually customized procedures. For example, it can present procedures based on the user's experience and skill level in a specific work. This makes it possible to provide optimal procedures based on the user's past work history.

[0074] The task support unit can collect real-time feedback from the user and dynamically adjust the procedures. For example, if the user finds a procedure difficult, the procedure can be changed immediately. In addition, the task support unit allows the generation AI to dynamically adjust the procedures based on the user's real-time feedback. For example, it analyzes the user's progress and reactions and provides the optimal procedure. In addition, the task support unit allows the generation AI to collect real-time feedback from the user and dynamically adjust the procedures. For example, it changes the difficulty or order of the procedures based on the user's feedback. This makes it possible to dynamically adjust the procedures based on the user's real-time feedback.

[0075] The business support unit can use the emotion estimation function to estimate the user's stress level and suggest relaxation methods if stress is high. For example, it can recommend deep breathing or a short break. The business support unit also uses the generation AI to monitor the user's stress level in real time and suggest relaxation methods if stress is high. For example, it can play relaxing music or provide a meditation guide. The business support unit can also use the emotion estimation function to estimate the user's stress level and suggest relaxation methods if stress is high. For example, it can recommend stretching or light exercise. This makes it possible to suggest relaxation methods according to the user's stress level.

[0076] The business support department can add a voice assistant function to respond to voice instructions and questions. For example, when a user asks a question by voice, the generation AI provides a voice answer. The business support department also uses the voice assistant function to have the generation AI respond to voice instructions and questions when assisting with business. For example, when a user gives instructions by voice, the generation AI explains the procedure by voice. The business support department also adds a voice assistant function to the generation AI to respond to voice instructions and questions. For example, when a user reports the progress of work by voice, the generation AI provides feedback by voice. This makes it possible to respond to voice instructions and questions.

[0077] The business support department can integrate a chat function to promote collaboration with other employees. For example, a chat function can be integrated into the generation AI to promote collaboration with other employees. For example, the generation AI can ask questions to other employees through chat. The business support department can also use the chat function to promote collaboration with other employees when the generation AI is assisting with work. For example, the generation AI can share the progress of work with other employees through chat. The business support department can also integrate a chat function into the generation AI to promote collaboration with other employees. For example, the generation AI can collect feedback from other employees through chat. This can promote collaboration with other employees.

[0078] The work support unit can use the emotion estimation function to provide messages that motivate the user so that they have positive feelings about their work. For example, it can send an encouraging message such as, "Great job!" The work support unit also uses the generation AI to monitor the user's emotional state and provide messages that motivate the user so that they have positive feelings. For example, it can send a message such as, "Your efforts are paying off." The work support unit can also use the emotion estimation function to provide messages that motivate the user so that they have positive feelings about their work. For example, it can send a message such as, "I can see your growth." This makes it possible to provide messages that motivate the user.

[0079] The growth measurement unit can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, the generation AI can integrate the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth can be measured by combining the self-assessment score with evaluations by superiors and colleagues. The growth measurement unit also analyzes the user's self-assessment and evaluations by others, and the generation AI can provide a comprehensive growth indicator. For example, the content of the self-assessment can be integrated with feedback from evaluations by others. The growth measurement unit also integrates the user's self-assessment and evaluations by others to provide a comprehensive growth indicator. For example, growth can be measured by combining strengths in the self-assessment and areas for improvement in evaluations by others. In this way, the user's self-assessment and evaluations by others can be integrated to provide a comprehensive growth indicator.

[0080] The growth measurement unit can measure growth by taking into account the user's activities outside of work. For example, the generation AI can measure growth by taking into account the user's activities outside of work. For example, it can provide growth indicators based on participation history in training and study sessions. The growth measurement unit also collects data on activities outside of work, which the generation AI can reflect in growth measurement. For example, it can take into account the content of seminars and workshops that the user has attended. The growth measurement unit can also measure growth by taking into account the user's activities outside of work. For example, it can include self-study and hobby activities in the growth indicators. This allows growth measurement to be performed by taking into account the user's activities outside of work.

[0081] The growth measurement unit can use the emotion estimation function to measure the user's motivation and satisfaction and reflect this in the growth index. For example, it can evaluate the degree of growth based on the user's emotion score. The growth measurement unit also uses the generation AI to monitor the user's motivation and satisfaction in real time and reflect this in the growth index. For example, it can determine that growth is progressing if there are a lot of positive emotions. The growth measurement unit also uses the emotion estimation function to measure the user's motivation and satisfaction and reflect this in the growth index. For example, it can evaluate the progress of growth based on changes in the user's emotions. This makes it possible to reflect the user's motivation and satisfaction in the growth index.

[0082] The growth measurement unit can provide comparative data with other employees and evaluate relative growth. For example, the generation AI can provide comparative data with other employees and evaluate relative growth. For example, it can compare with the growth data of other employees who are in charge of the same work. The growth measurement unit also allows the generation AI to evaluate the user's relative growth based on the growth data of other employees. For example, it can compare growth rates over the same period. The growth measurement unit also allows the generation AI to provide comparative data with other employees and evaluate relative growth. For example, it can analyze the difference in growth with employees who have the same skill set. This allows it to provide comparative data with other employees and evaluate relative growth.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The work support department provides new employees and mid-career employees with the information and procedures they need to carry out their work. For example, when a new employee performs a task for the first time, the generation AI presents specific steps such as, "To perform this task, first do A, then do B." The generation AI also provides appropriate information based on prompts containing instructions on what the user wants the generation AI to do. Step 2: The Growth Measurement Department monitors the work performance of new and mid-career employees and measures their growth. For example, the Generative AI analyzes work progress and deliverables and provides growth indicators such as, "This employee's work efficiency has improved by 20% over the past month." The Generative AI measures growth based on data on work progress and deliverables.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that uses generative AI to support the work of new employees and mid-career employees and measure their growth. The Business Support Department provides new and mid-career employees with the information and procedures they need to carry out their work. A growth measurement department monitors the work performance of new employees and mid-career employees and measures their growth. A system characterized by:

2. The business support department Refer to the user's past work history and provide individually customized procedures 2. The system of claim 1.

3. The business support department Gather real-time user feedback and dynamically adjust procedures 2. The system of claim 1.

4. The business support department Estimate the user's stress level and suggest relaxation methods if the stress level is high 2. The system of claim 1.

5. The business support department Adds voice assistant functionality to respond to voice commands and questions 2. The system of claim 1.

6. The business support department Integrate chat functionality to facilitate collaboration with other employees 2. The system of claim 1.

7. The business support department Provide motivational messages to encourage users to have positive feelings about their work 2. The system of claim 1.

8. The growth measurement unit Integrates users' self-assessments and those of others to provide comprehensive growth indicators 2. The system of claim 1.

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